What is real-time data?
Real-time data is made available for use as soon as it is acquired. The use of real-time data is particularly seen in newer technologies that work on delivering to-the-minute data to convenience apps that are used on personal devices or work-related ones.
Real-time data works on the basic principle that it is not stored or kept in silos. Instead, it moves on directly to the end user. This delivery does not mean that the data reaches the user instantly. There may be several impediments to that, such as a weakness in the data infrastructure or a difference in bandwidth between the receiver and the sender. What real-time data essentially means is that the data is not held back when it is collected.
There are several uses for real-time data—for example, to help cab drivers understand traffic situations. Instant data delivery assists in a wide range of analytic projects and other business activities requiring fast, easy data access.
How does real-time data work?
Real-time data facilitates ulta-fast, continuous data analytics. There is a short duration from the receiving data, to its transmission, to the end point. Nevertheless, it goes through four major steps:
1. Capture of streaming data:
Live streaming data is captured using scrapers (automated process for web data gathering), collectors (application that collects and delivers analyzed meta-data), agents (used to collect vast amounts of data), listeners (programs that get notified of new data before it hits the backend), and is stored in a NoSQL database (stores data in a non-tabular manner).
These databases may be similar to Cassandra, MongoDB, or even Hadoop’s Hive.
2. Stream processing of data
Streaming data is then processed in a variety of ways, but ultimately involves splitting, merging, performing calculations, and making it connect with external data sources. A good database system should help with this step. Commonly after this stage, the data is ready for the visualization component. However, now there are new technologies that allow us to visualize real-time data without having to go to the database first.
Many common big data processing frameworks have not really been used for real-time data analytics until recently. This is due to the increased demand of real-time data, forcing software engineers to make programs compatible with real-time analytics.
3. Visualization of processed data:
Processed data is stored in specific structured formats, often the likes of JSON or XML in a NoSQL database. It is from this database that the information is read by the visualization component. Internal business intelligence (BI) systems will have a charting library that enables the visualization component. The visualization component reads data from the structured data file and creates charts, gauges, or other forms of visualization that connect to the interface.
4. From visualization to the real-time dashboard
Data is constantly refreshed in the JSON or XML file, and the frequency of this is called the update interval. The frequency at which this processed data is drawn by the recipient client is known as the refresh interval. If, for example, a stock trading application is using the data and visualizations, it can trigger some pre-set rules based on what the streaming data shows.
This entire process takes place in a matter of milliseconds and has been enabled by advances in database technology, specifically NoSQL databases. Query tools enable the process. Visualization tools have grown to support the growing demands of a range of scenarios that require real-time data, supporting an ever-evolving ecosystem of real-time analytics for a range of big data applications.
5. Scenarios where real-time data helps
Real-time data can make a difference in a wide range of businesses and how their operations are executed. Employing real-time data fundamentally changes how any business makes decisions and adapts to changes in their data.
Improvement in customer service
A customer calling a helpline does not want to be kept waiting. Additionally, they don’t want to spend time repeating their customer information from the previous call or as they get passed around departments.
Real-time data dashboards can help quickly select an idle customer care executive to take the call, reducing waiting time. The dashboard can then bring up all related customer information, which allows the executive to get to the point quicker and help address the issue. The system can also, in real-time, figure out if the assistance of a supervisor is needed.
Provide overviews to managers
Managers are often called on to improve the efficiency of a system. The dashboard with real-time data gives them an overview of the entire functioning system, including bottlenecks, delays in hold time, and volume tracking. All of these elements can be evaluated and be used to enhance the efficacy of the working system and bring it up to higher standards.
Improvement in operational efficiency
Operational efficiency has to happen at multiple levels: inventory, dispatch, supply-chain, delivery, and reception. One glitch at any level can be a massive disruption. Real-time data keeps track of every level and ensures that the right authorities are informed when something goes wrong. Such a consistent flow of information prevents a slowing down of production, helps deal with a delay in materials arriving, prevents unnecessary re-supply of materials, and much more.
With real-time data enhancing employee methods, workers are able to make the necessary changes in a much simpler, quicker way. It also helps improve their efficiency and see results immediately. Furthermore, real-time dashboards can be interactive or gamified, making it fun for employees to interact with and perhaps setting up some friendly competition.
Improving employee performance
In every organization, there are high performers and there are those that have the potential to improve. Those with potential often need a little hand holding to help them get there. Real-time data helps managers identify top performers and those who may need some help. The high performers can be recognized for their good work and those needing some assistance can be provided with the training and resources they require.
Benefits of real-time big data analytics
There are several benefits for an organization that processes data in real-time, including:
- Insight into errors: Knowing about an error in real time can help an organization deal with it instantly, thereby reducing the impact on the business. Any operational problem can be solved if it is immediately brought to light, and this can prevent operations slowing down and costing the company.
- Real-time updates on competitor strategies: Knowing what your competitor is up to as soon as they implement a new tactic can give you time in which to re-strategize and possibly help stay a step ahead of the game.
- Dramatic improvement in service: Real-time data analytics gives a business an opportunity to evolve quickly and leads to a much higher conversion rate and revenue. For example, for Internet connected cars, the owner of the car can be notified if it is found that a component of the vehicle is not working optimally and it can be repaired before it causes greater damage.
- Instant cybercrime detection: With real-time data enabled safety measures in place, you will know of a possible cyber-attack instantly and be able to take measures to contain it. This empowers the IT department of your organization to better protect information security.
- Cost savings: The initial cost of real-time data analytics may be high, but the return on investment is quick, saving the organization a great deal of money. It also brings down the burden on the IT infrastructure of the company, allowing for quicker, more targeted responses when and where they are needed.
- Better sales insights: With real-time analytics, you have better sales insights, which naturally lead to a higher revenue. With the data, companies will be able to evaluate sales in real time, e-commerce sites will be able to see how a product fares, and customer purchasing patterns will help a business anticipate needs of the market.
The challenges of real-time big data analytics
There are a number of challenges in realizing real-time data in business. A few are outlined below:
Specialized computing power
Legacy systems do not lend themselves well to real-time data analytics. Which means, a business will have to buy newer tools to get the job done. But the results and benefits as discussed above far outweigh the costs associated with onboarding new technologies. These updates are necessary regardless, as businesses that fail to evolve will be left behind and unable to keep up with competitors that are employing real-time data analytics to great effect.
Change in organizational functioning
The use of real-time data necessitates that an organization works in a different way than it may be accustomed. Most organizations work on weekly review meetings to be able to handle any issues. With real-time data, you are looking at information coming in every few minutes, if not seconds. This requires a specialized approach to work processes. What was once a weekly change in approach may now become daily, affecting company culture. Making your organization information-centric is the first step forward in enabling this real-time, data-driven decision making environment.
Real-time data can dramatically change the way businesses work. The approach to its implementation has to be systematic. The benefits can be extraordinary though, enabling growth and exceptional service to all stakeholders.
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